Identifying subgroups with similar survival outcomes is a pivotal challenge in survival
analysis. Traditional clustering methods often neglect the outcome variable, potentially …

Survival analysis studies and predicts the time of death, or other singular unrepeated events,
based on historical data, while the true time of death for some instances is unknown …

Survival analysis studies time-modeling techniques for an event of interest occurring for a
population. Survival analysis found widespread applications in healthcare, engineering, and …

Survival analysis is a general framework for predicting the time until a specific event occurs,
often in the presence of censoring. Although this framework is widely used in practice, few …

Survival analysis is an important research topic with applications in healthcare, business,
and manufacturing. One essential tool in this area is the Cox proportional hazards (CPH) …

Survival Analysis (SA) is about modeling the time for an event of interest to occur, which has
important applications in many fields, including medicine, defense, finance, and aerospace …

This work presents the first large-scale neutral benchmark experiment focused on single-
event, right-censored, low-dimensional survival data. Benchmark experiments are essential …

Survival analysis is an essential tool in healthcare for risk assessment, assisting clinicians in
their evaluation and decision making processes. Therefore, the importance of using …

Survival analysis is employed to scrutinize time-to-event data, with emphasis on
comprehending the duration until the occurrence of a specific event. In this article, we …

Conformal predictive systems output predictions in the form of well-calibrated cumulative
distribution functions (conformal predictive distributions). In this paper, we apply conformal …